Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Oncology
Population-Specific Mutation Patterns In Breast Tumors From African American, European American, And Kenyan Patients, Wei Tang, Jung S. Byun, Tiffany H. Dorsey, Harris G. Yfantis, Anuoluwapo Ajao, Huaitian Liu, Margaret S. Pichardo, Catherine M. Pichardo, Alexandra R. Harris, Shahin Sayed
Population-Specific Mutation Patterns In Breast Tumors From African American, European American, And Kenyan Patients, Wei Tang, Jung S. Byun, Tiffany H. Dorsey, Harris G. Yfantis, Anuoluwapo Ajao, Huaitian Liu, Margaret S. Pichardo, Catherine M. Pichardo, Alexandra R. Harris, Shahin Sayed
Pathology, East Africa
Women of African descent have the highest breast cancer mortality in the United States and are more likely than women from other population groups to develop an aggressive disease. It remains uncertain to what extent breast cancer in Africa is reminiscent of breast cancer in African American or European American patients. Here, we performed whole-exome sequencing of genomic DNA from 191 breast tumor and non-cancerous adjacent tissue pairs obtained from 97 African American, 69 European American, 2 Asian American, and 23 Kenyan patients. Our analysis of the sequencing data revealed an elevated tumor mutational burden in both Kenyan and African …
Pitfalls In Machine Learning-Based Assessment Of Tumor-Infiltrating Lymphocytes In Breast Cancer: A Report Of The International Immuno-Oncology Biomarker Working Group On Breast Cancer, Jeppe Thagaard, Glenn Broeckx, Chowdhury Arif Jahangir, Sara Verbandt, Rajarsi Gupta, Reena Khiroya, Khalid Abduljabbar, Gabriela Acosta Haab, Balazs Acs, Shahin Sayed
Pitfalls In Machine Learning-Based Assessment Of Tumor-Infiltrating Lymphocytes In Breast Cancer: A Report Of The International Immuno-Oncology Biomarker Working Group On Breast Cancer, Jeppe Thagaard, Glenn Broeckx, Chowdhury Arif Jahangir, Sara Verbandt, Rajarsi Gupta, Reena Khiroya, Khalid Abduljabbar, Gabriela Acosta Haab, Balazs Acs, Shahin Sayed
Pathology, East Africa
Abstract: The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state-of-the-art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL …